Causal associations of genetically predicted gut microbiota and blood metabolites with inflammatory states and risk of infections: a Mendelian randomization analysis.

Background: Inflammation serves as a key pathologic mediator in the progression of infections and various diseases, involving significant alterations in the gut microbiome and metabolism. This study aims to probe into the potential causal relationships between gut microbial taxa and human blood metabolites with various serum inflammatory markers (CRP, SAA1, IL-6, TNF-α, WBC, and GlycA) and the risks of seven common infections (gastrointestinal infections, dysentery, pneumonia, bacterial pneumonia, bronchopneumonia and lung abscess, pneumococcal pneumonia, and urinary tract infections). Methods: Two-sample Mendelian randomization (MR) analysis was performed using inverse variance weighted (IVW), maximum likelihood, MR-Egger, weighted median, and MR-PRESSO. Results: After adding other MR models and sensitivity analyses, genus Roseburia was simultaneously associated adversely with CRP (Beta IVW = -0.040) and SAA1 (Beta IVW = -0.280), and family Bifidobacteriaceae was negatively associated with both CRP (Beta IVW = -0.034) and pneumonia risk (Beta IVW = -0.391). After correction by FDR, only glutaroyl carnitine remained significantly associated with elevated CRP levels (Beta IVW = 0.112). Additionally, threonine (Beta IVW = 0.200) and 1-heptadecanoylglycerophosphocholine (Beta IVW = -0.246) were found to be significantly associated with WBC levels. Three metabolites showed similar causal effects on different inflammatory markers or infectious phenotypes, stearidonate (18:4n3) was negatively related to SAA1 and urinary tract infections, and 5-oxoproline contributed to elevated IL-6 and SAA1 levels. In addition, 7-methylguanine showed a positive correlation with dysentery and bacterial pneumonia. Conclusion: This study provides novel evidence confirming the causal effects of the gut microbiome and the plasma metabolite profile on inflammation and the risk of infection. These potential molecular alterations may aid in the development of new targets for the intervention and management of disorders associated with inflammation and infections.

Exploring the causal effects of the gut microbiome on serum lipid levels: A two-sample Mendelian randomization analysis.

Background: The gut microbiome was reported to be associated with dyslipidemia in previous observational studies. However, whether the composition of the gut microbiome has a causal effect on serum lipid levels remains unclear. Objective: A two-sample Mendelian randomization (MR) analysis was conducted to investigate the potential causal relationships between gut microbial taxa and serum lipid levels, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and log-transformed triglyceride (TG) levels. Materials and methods: Summary statistics of genome-wide association studies (GWASs) for the gut microbiome and four blood lipid traits were obtained from public datasets. Five recognized MR methods were applied to assess the causal estimates, among which, the inverse-variance weighted (IVW) regression was used as the primary MR method. A series of sensitivity analyses were performed to test the robustness of the causal estimates. Results: The combined results from the five MR methods and sensitivity analysis showed 59 suggestive causal associations and four significant causal associations. In particular, genus Terrisporobacter was associated with higher LDL-C (P IVW = 3.01 × 10-6) and TC levels (P IVW = 2.11 × 10-4), phylum Actinobacteria was correlated with higher LDL-C level (P IVW = 4.10 × 10-4), and genus Oscillospira was associated with lower TG level (P IVW = 2.19 × 10-6). Conclusion: This research may provide novel insights into the causal relationships of the gut microbiome on serum lipid levels and new therapeutic or prevention strategies for dyslipidemia.

A consortium of three-bacteria isolated from human feces inhibits formation of atherosclerotic deposits and lowers lipid levels in a mouse model.

By a survey of metagenome-wide association studies (MWAS), we found a robust depletion of Bacteroides cellulosilyticus, Faecalibacterium prausnitzii, and Roseburia intestinalis in individuals with atherosclerotic cardiovascular disease (ACVD). From an established collection of bacteria isolated from healthy Chinese individuals, we selected B. cellulosilyticus, R. intestinalis, and Faecalibacterium longum, a bacterium related to F. prausnitzii, and tested the effects of these bacteria in an Apoe/- atherosclerosis mouse model. We show that administration of these three bacterial species to Apoe-/- mice robustly improves cardiac function, reduces plasma lipid levels, and attenuates the formation of atherosclerotic plaques. Comprehensive analysis of gut microbiota, plasma metabolome, and liver transcriptome revealed that the beneficial effects are associated with a modulation of the gut microbiota linked to a 7α-dehydroxylation-lithocholic acid (LCA)-farnesoid X receptor (FXR) pathway. Our study provides insights into transcriptional and metabolic impact whereby specific bacteria may hold promises for prevention/treatment of ACVD.

Comprehensive Metabolic Profiling of Inflammation Indicated Key Roles of Glycerophospholipid and Arginine Metabolism in Coronary Artery Disease.

Background: Systemic immune inflammation is a key mediator in the progression of coronary artery disease (CAD), concerning various metabolic and lipid changes. In this study, the relationship between the inflammatory index and metabolic profile in patients with CAD was investigated to provide deep insights into metabolic disturbances related to inflammation. Methods: Widely targeted plasma metabolomic and lipidomic profiling was performed in 1,234 patients with CAD. Laboratory circulating inflammatory markers were mainly used to define general systemic immune and low-grade inflammatory states. Multivariable-adjusted linear regression was adopted to assess the associations between 860 metabolites and 7 inflammatory markers. Least absolute shrinkage and selection operator (LASSO) logistic-based classifiers and multivariable logistic regression were applied to identify biomarkers of inflammatory states and develop models for discriminating an advanced inflammatory state. Results: Multiple metabolites and lipid species were linearly associated with the seven inflammatory markers [false discovery rate (FDR) <0.05]. LASSO and multivariable-adjusted logistic regression analysis identified significant associations between 45 metabolites and systemic immune-inflammation index, 46 metabolites and neutrophil-lymphocyte ratio states, 32 metabolites and low-grade inflammation score, and 26 metabolites and high-sensitivity C-reactive protein states (P < 0.05). Glycerophospholipid metabolism and arginine and proline metabolism were determined as key altered metabolic pathways for systemic immune and low-grade inflammatory states. Predictive models based solely on metabolite combinations showed feasibility (area under the curve: 0.81 to 0.88) for discriminating the four parameters that represent inflammatory states and were successfully validated using a validation cohort. The inflammation-associated metabolite, namely, ß-pseudouridine, was related to carotid and coronary arteriosclerosis indicators (P < 0.05). Conclusions: This study provides further information on the relationship between plasma metabolite profiles and inflammatory states represented by various inflammatory markers in CAD. These metabolic markers provide potential insights into pathological changes during CAD progression and may aid in the development of therapeutic targets.

Using a Two-Sample Mendelian Randomization Method in Assessing the Causal Relationships Between Human Blood Metabolites and Heart Failure.

Background: Heart failure (HF) is the main cause of morbidity and mortality worldwide, and metabolic dysfunction is an important factor related to HF pathogenesis and development. However, the causal effect of blood metabolites on HF remains unclear. Objectives: Our chief aim is to investigate the causal relationships between human blood metabolites and HF risk. Methods: We used an unbiased two-sample Mendelian randomization (MR) approach to assess the causal relationships between 486 human blood metabolites and HF risk. Exposure information was obtained from Sample 1, which is the largest metabolome-based genome-wide association study (mGWAS) data containing 7,824 Europeans. Outcome information was obtained from Sample 2, which is based on the results of a large-scale GWAS meta-analysis of HF and contains 47,309 cases and 930,014 controls of Europeans. The inverse variance weighted (IVW) model was used as the primary two-sample MR analysis method and followed the sensitivity analyses, including heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. Results: We observed that 11 known metabolites were potentially related to the risk of HF after using the IVW method (P < 0.05). After adding another four MR models and performing sensitivity analyses, we found a 1-SD increase in the xenobiotics 4-vinylphenol sulfate was associated with ~22% higher risk of HF (OR [95%CI], 1.22 [1.07-1.38]). Conclusions: We revealed that the 4-vinylphenol sulfate may nominally increase the risk of HF by 22% after using a two-sample MR approach. Our findings may provide novel insights into the pathogenesis underlying HF and novel strategies for HF prevention.